Abstract
Purpose:
Tobacco use during early adolescence can harm brain development and cause adverse health outcomes. Identifying susceptibility in early adolescence before initiation presents an opportunity for tobacco use prevention.
Methods:
Data were drawn from the Adolescent Brain and Cognitive Development study that enrolled 9–10-year-old children in 21 US cities between 2016 and 2018 at baseline. Separate nested hierarchical models were performed to incrementally examine the associations of sociodemographic factors, psychosocial influences, parental substance use, immediate social contacts, and perceived neighborhood safety with tobacco use susceptibility among never tobacco users (n = 10,449), overall and stratified by gender.
Results:
A total of 16.6% of youths who have never used tobacco reported susceptibility to tobacco. Females (vs. males, adjusted odds ratio [AOR] [95% confidence interval {CI}] = 0.80 [0.70–0.91]), positive parental monitoring (AOR [95% CI] = 0.76 [0.66–0.87]) and positive school environment (AOR [95% CI] = 0.95 [0.93–0.98]) were associated with reduced susceptibility to tobacco use. Parental education level (high school, AOR [95% CI] = 1.52 [1.02–2.28]; bachelor’s degree, AOR [95% CI] = 1.53 [1.03–2.28]; or postgraduate degree, AOR [95% CI] = 1.54 [1.03–2.3] vs. less than high school), youth substance ever use (AOR [95% CI] = 2.24 [1.95–2.58]), internalizing problems (AOR [95% CI] = 1.03 [1–1.06]), and high scores on negative urgency, lack of premeditation, lack of perseverance, sensation seeking, and positive urgency-impulsive behavior scale were associated with increased susceptibility to tobacco use. Stratified analysis showed that parent-perceived neighborhood safety was associated with reduced susceptibility to tobacco use among males but not among females (AOR [95% CI] = 0.89 [0.81–0.99]) vs. (AOR [95% CI] = 1.01 [0.9–1.13]). A positive school environment was associated with lower susceptibility to tobacco use among females but not among males.
Discussion:
Parental, environmental, and psychosocial factors influence early childhood tobacco susceptibility. Family and school-based tobacco prevention programs should consider integrating these factors into primary school curricula to reduce youth tobacco susceptibility and later initiation.
Keywords: The Adolescent Brain and Cognitive Development (ABCD) study, Tobacco use susceptibility, Early year, Nested hierarchical model
Preventing the initiation of tobacco use among US youth is a public health priority and has led to an emphasis on interventions to prevent never tobacco users from initiating any tobacco use [1]. In 2013–14, the nationally representative Population Assessment of Tobacco and Health study reported that the 95% quartiles for age of first use of a tobacco product reported ranged from 7–23 years [2]. Among those aged 12 years, 5% were already tobacco users and 36% were susceptible to start using tobacco [3]. Most US national studies do not include students below middle school or younger than 12 years old [4,5], and so are limited in their usefulness for understanding the earliest phases of tobacco initiation. An exception is the Adolescent Brain and Cognitive Development (ABCD) study that enrolled a large US cohort of preteens aged 9–10 years and investigated susceptibility to use tobacco, among many other constructs [6].
The susceptibility to tobacco use construct, used in the ABCD study, was originally developed by Pierce et al. [7] to distinguish between committed never smokers and those who might be cognitively predisposed to smoking, based on their intentions as well as their self-efficacy to resist an offer [8]. Baseline susceptibility, defined as the absence of a firm decision not to smoke, was shown to have predictive validity for experimentation within four years in a national cohort study [7]. As the major goal of advertising is to increase curiosity [9], and tobacco advertising has been associated with initiation [10], the construct “curiosity about smoking” was added to make an enhanced susceptibility construct [11]. This enhanced construct increased the size of the population identified as “at risk to use” (sensitivity) while maintaining the positive predictive value of the construct [12,13]. This three-item construct has been used in most recent national surveillance systems and product specific susceptibility has been shown to predict future use of e-cigarettes, cigars, and smokeless tobacco as well as cigarettes [14].
The tobacco use landscape has substantially changed in the last decade, with more adolescents using e-cigarettes and other emerging tobacco products (e.g., hookah) [15,16]. In 2014, e-cigarettes surpassed cigarettes to become the most prevalent tobacco product used by the US youth [15], and e-cigarette companies have aggressively marketed e-cigarettes with a sleek design, high levels of nicotine concentration, and a plethora of flavors (e.g., fruit, sweet, mint, and ice) that are appealing to youth [17]. Recent studies also reported that youth susceptible to e-cigarette use were four times more likely to initiate e-cigarettes subsequently than those who were not susceptible [14,18].
The social cognitive theory (SCT) [19] postulates that changes in cognition, such as moving from a committed never tobacco user to the one who is susceptible to use, are influenced by variables associated with the individual, the behavior itself, and the social/physical environment. Examples of individual variables associated with susceptibility to use tobacco include age, gender, race/ethnicity, and parental education level [3]. Susceptibility is also higher in teens who are sensation seekers or who have used other substances [20], as well as those with symptoms of mental health problems (e.g., internalizing and externalizing problem behaviors) [21]. Examples of social/physical environment variables associated with susceptibility to tobacco use include exposure to smoking among friends or family [8], parenting practices [22], and the school and neighborhood environment [23]. Factors such as smoking among social peers or family provide observational learning opportunities through ‘peer learning’ or ‘role modeling’ of tobacco use which increases awareness of how and when to smoke, as well as how to obtain tobacco products, which increases the behavioral capability of an adolescent to actually initiate smoking [24]. According to the SCT, this social environment also shapes the expectations of future use in adolescents, by framing smoking outcomes as a normative social behavior rather than an addicting, adverse medical decision [25]. However, few studies have comprehensively assessed incremental effects of individual, immediate social contexts, and the environment on early-age tobacco use susceptibility. There are also pronounced gender differences in developmental-related neurobiological vulnerability to substance use initiation and social-related risk factors for tobacco use among adolescents [1,26]. Male adolescents report higher risks of tobacco use susceptibility than their female counterparts [27]. It is unclear how these multifaceted factors may differently influence initiation for males as compared to females at an early age.
This study analyzed the ABCD baseline data to (1) determine the proportion of early-age youths (9–10 years of age) who are susceptible to tobacco use; (2) investigate a large number of risk and protective factors associated with early-age tobacco use susceptibility; and (3) examine any gender differences in associations between these influencing factor and tobacco use susceptibility. We hypothesized that these individual and social/physical environmental factors incrementally affect tobacco use susceptibility with differential effects for males and females.
Data and Measures
Data
Data for this study were obtained from the National Data Archive ABCD 3.0 release. The ABCD study leveraged a large cohort with 11,878 9- and 10-year old children enrolled at baseline across 21 US research sites between October 1, 2016 and October 31, 2018 [6]. Baseline participants were recruited through a probability sample of schools and were selected by assigned sex at birth, race/ethnicity, socioeconomic status, and urbanicity to maintain the sample demographics in accordance with the American Community Survey third and fourth grade enrollment statistics at each site [28]. Participants completed a comprehensive battery including clinical interviews, surveys, neurocognitive tests, and neuroimaging. At least one parent or guardian also completed separate surveys [28–30]. All parents or guardians provided written informed consent and children gave written assent. The study procedure was approved by the centralized Institutional Review Board of the University of California, San Diego and by the institutional review board at each local institution.
Measures
Susceptibility to tobacco use.
Participants were first asked whether they had heard of tobacco products, such as cigarettes, smokeless tobacco, cigars, hookah, electronic or e-cigarettes. Those who reported “yes” were asked whether they had ever tried any tobacco products in their life. Those who reported “no” were classified as never tobacco users and were asked, “Have you ever been curious about using a tobacco product such as cigarettes, e-cigarettes, hookah, or cigars?” with response options of “very curious,” “somewhat curious,” “a little curious,” and “not at all curious.” They were also asked “Do you think you will try a tobacco product soon?” and “If one of your best friends were to offer you a tobacco product, would you try it?” with response options “definitely yes,” “probably yes,” “probably not,” and “definitely not.” Those who reported “not at all curious” and “definitely not” to all three susceptibility questions were classified as “not susceptible to tobacco use.” [7].
Parent-reported sociodemographics factors.
Parents were asked their children’s age (continuous in months), sex at birth (male/female), and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, or other non-Hispanic classifications) as well as their highest education level (less than high school, high school diploma or general educational development (GED), some college or associate degree, bachelor’s degree, or postgraduate degree), family income (<$25,000, $25,000–$49,999, $50,000–$74,999, $75,000–$99,999, $100,000+, or do not know/refuse to answer), experience of any family difficulty in the past 12 months (7 items, e.g., “need food but could not afford it,” “did not pay the full amount of the rent or mortgage because you could not afford it,” Cronbach’s alpha = 0.91), and premature birth of the child (yes/no).
Substance use and psychosocial factors.
Such factors included youth self-reported ever substance use (e.g., marijuana, alcohol, and other illicit drugs; such as cocaine, methamphetamine, ecstasy/3,4-methyenedioxymethamphetamine, ketamine, gamma-hydroxybutyrate, heroin, psilocybin, salvia, other hallucinogens; anabolic steroids, inhalants, prescription stimulants, sedatives, opioid pain relievers, and over-the-counter cough/cold medicine) [31], parent-rated children’s internalizing behaviors (anxious/depressed, withdrawal/depressed, somatic complaints, Cronbach’s alpha = 0.74), externalizing behaviors (rule-breaking, aggressive, Cronbach’s alpha = 0.85), and total problems behaviors (internalizing and externalizing scales plus social problems, thought problems, attention problems, Cronbach’s alpha = 0.89) based on the Child Behavior Checklist (CBCL) [32]. The DSM-5 oriented CBCL subscale was calculated as the sum of six DSM-5-oriented diagnostic categories (affective problems, anxiety problems, somatic problems, attention-deficit/hyperactivity disorder, oppositional defiant problems, conduct problems, and Cronbach’s alpha = 0.83) [33]. Stress was measured by the 2007 CBCL stress problems scale [34], with higher scores indicative of greater psychopathology. All CBCL scales and subscales use a 3-point Likert scale (0 = absent, 1 = occurs sometimes, and 2 = occurs often).
The negative urgency, lack of premeditation, lack of perseverance, sensation seeking, and positive urgency (UPPS-P) impulsive behavior scale comprises five impulsive personality traits: negative urgency, lack of premeditation, sensation seeking, positive urgency, and lack of persistence subscales [35].
Parental substance use.
Parents were asked about their tobacco use during pregnancy (yes/no), past six-month use of tobacco products (yes/no), ≥1 day being drunk (yes/no), and use of drugs for nonmedical purposes (including marijuana, cocaine, and other drugs, except alcohol and nicotine) (yes/no) in the past 6 months.
Family and proximal school environment.
These questions included the acceptance subscale from Children’s Report of Parenting Behavior Inventory—Short (5 items, e.g., “…smiles at me very often,” “…is easy to talk to”, Cronbach’s alpha = 0.72) [36], child-reported parent monitoring scale (5 items, e.g., “How often do your parents know where you are?”, Cronbach’s alpha = 0.50) [37] and school risk and protective factors school environment subscale (6 items, e.g., “I feel safe at my school.” Cronbach’s alpha = 0.62) [38] Higher scores indicate stronger family ties and parental monitoring, or a favorable school environment.
Perceived neighborhood safety and crime.
These questions assessed feelings about safety and the presence of crime in the respondent’s neighborhood [39], including measures from the youth (one item with a 5-point Likert scale, “My neighborhood is safe from crime”) as well as the parents (average of three items with a 5-point Likert scale, “I feel safe walking in my neighborhood, day or night.” Violence is not a problem in my neighborhood.” and “My neighborhood is safe from crime.” Cronbach’s alpha = 0.89) [40] Higher scores indicated a safer perceived neighborhood (1-strongly disagree, 5-strongly agree).
Statistical methods
A cross-sectional analysis was performed using the ABCD baseline data. Weighted descriptive statistics of participant characteristics were reported overall and stratified by baseline tobacco use susceptibility. The internal consistency and reliability of survey scales were assessed by Cronbach’s alpha. Rao-Scott chi-square tests were performed to detect group differences [41]. Individual subject’s inverse probability weighting score was included as sampling weights in all statistical analyses to account for nonresponsiveness and ensure population-valid estimates [28,42]. In the bivariate analysis, unadjusted odds ratios are presented to measure the associations between influencing factors and tobacco use susceptibility. Separate nested hierarchical multivariate logistic models [43,44] were performed to incrementally adjust for the effects of sociodemographics, youth substance use and psychosocial factors, parental substance use, family and proximal school environment factors, and perceived neighborhood safety in the prediction of tobacco use susceptibility, stratified by males and females. Hierarchical models have been commonly used to analyze high-dimensional influencing variables by (1) comparing the goodness-of-fit statistics between parsimonious and comprehensive models and (2) assessing associations between the explanatory variable and the outcome after gradual adjustment of other confounding variables [44,45]. Based on the SCT [19], we created a hierarchy from a reduced model with individual and proximal factors to advanced models including environmental and distal factors.
Adjusted odds ratios (AOR) and confidence intervals (CIs) [46] are presented with the study site treated as the fixed effect in the multivariable analysis. We compared the model goodness-of-fit and predictive ability using concordance (c)-statistic, calculated by the area under the receiver operating characteristic curve. Statistical analyses were performed using SAS 9.4 (Cary, NC). A p-value <.05 was considered statistically significant.
Results
As depicted in Figure A1, the ABCD study included 11,878 adolescents at baseline. After excluding 793 participants who reported “never heard” of tobacco products, 118 participants who reported ever tobacco use (i.e., cigarettes, e-cigarettes, cigars, smokeless/chew, hookah, and pipe, n = 117) in their lifetime or had missing tobacco use status (n = 1) at baseline, and 518 participants with missing information on susceptibility to tobacco use, and 10,449 never tobacco users were included in the final analytical sample.
The analytical sample was sociodemographically diverse, including 48.9% female, 54.6% non-Hispanic Whites, 12.4% Blacks, 22.3% Hispanics, 3.6% Asians, 7.0% other or multi-racial races. The mean age of participants’ was 119.5 months (or 9.96 years), 15.3% of participants reported family difficulties in the past 12 months, and perceived neighborhood safety/crime had a mean of 4.0 and 3.9 for children and parents, respectively. Overall, 16.6% of 9–10-year-old adolescents reported susceptibility to tobacco use. The sample sociodemographic characteristics were similar in tobacco use susceptibility except for gender, parental education level, and child’s perceived neighborhood safety (Table 1).
Table 1.
Participant characteristics of the baseline ABCD study (N = 10,449)
| Variable | Susceptibility to tobacco use | |||
|---|---|---|---|---|
|
|
||||
| Analytical sample | No | Yes | p-value | |
|
| ||||
| N (Weighted %) | 10,449 | 8715 (83.4) | 1734 (16.6) | |
| Age, mean (SE), months | 119.5 (0.08) | 119.5 (0.1) | 119.3 (0.2) | .41 |
| Gender | <.0001 | |||
| Male | 5455 (51.1) | 4415 (49.7) | 1040 (58.5) | |
| Female | 4994 (48.9) | 4300 (50.3) | 694 (41.5) | |
| Race/Ethnicity | .38 | |||
| White | 5696 (54.6) | 4762 (54.5) | 931 (55.9) | |
| Black | 1448 (12.4) | 1218 (12.6) | 228 (11.2) | |
| Hispanics | 1976 (22.3) | 1658 (22.5) | 318 (21.6) | |
| Asians | 221 (3.6) | 184 (3.7) | 39 (3.6) | |
| Othera | 1107 (7.0) | 875 (6.7) | 215 (7.7) | |
| Parental education level | .02 | |||
| Less than high school | 491 (5.8) | 425 (6.1) | 66 (4.3) | |
| High school diploma or GED | 962 (11.0) | 789 (10.7) | 173 (12.2) | |
| Some college or associate degree | 2577 (28.2) | 2174 (28.6) | 403 (26.3) | |
| Bachelor’s degree | 2930 (26.4) | 2426 (26.1) | 504 (27.8) | |
| Postgraduate degree | 3489 (28.6) | 2901 (28.4) | 588 (29.2) | |
| Family income | .43 | |||
| <$25,000 | 1295 (15.7) | 1074 (15.7) | 221 (15.9) | |
| $25,000–$49,999 | 1351 (17.9) | 1141 (18.1) | 210 (16.5) | |
| $50,000–$74,999 | 1321 (16.0) | 1080 (15.8) | 241 (17.2) | |
| $75,000–$99,999 | 1418 (12.7) | 1175 (12.6) | 243 (13.7) | |
| $100,000+ | 4234 (28.8) | 3552 (28.9) | 682 (28.3) | |
| Don’t know or refuse to answer | 830 (8.9) | 693 (9.0) | 137 (8.3) | |
| Family Difficulty | .64 | |||
| No | 9129 (84.7) | 7627 (84.8) | 1502 (84.2) | |
| Yes | 1320 (15.3) | 1088 (15.2) | 232 (15.8) | |
| Premature | .19 | |||
| No | 8422 (81.5) | 7055 (81.7) | 1367 (80.2) | |
| Yes | 1910 (18.5) | 1570 (18.3) | 340 (19.8) | |
| Neighborhood perceptions, mean (SE) | ||||
| Child | 4.0 (0.01) | 4.0 (0.01) | 3.9 (0.03) | .002 |
| Parent | 3.9 (0.01) | 3.9 (0.01) | 3.8 (0.03) | .23 |
ABCD = adolescent brain cognitive development; GED = general educational development; OR = odds ratio; SE = standard error.
The other category, based on the ABCD study definition, includes American Indian, Alaskan Native, Native Hawaiian, and other Pacific Islander as well as not otherwise listed (other) and multiracial individuals.
Table A1 presents the bivariate associations of influencing factors and early-age tobacco use susceptibility. Females (vs. males, odds ratio [OR] [95% CI] = 0.70 [0.62e0.79]), positive parenting (acceptance scale, OR [95% CI] = 0.51 [0.43–0.60]), positive parental monitoring (OR [95% CI] = 0.59 [0.53–0.65]), favorable school environment (OR [95% CI] = 0.91 [0.89–0.93]), and child’s perception of living in a safe neighborhood (OR [95% CI] = 0.92 [0.88–0.97]) were associated with a lower likelihood of tobacco use susceptibility. Higher parental education (high school diploma or GED or bachelor’s degree or postgraduate degree vs. less than high school), youth ever substance use, maternal tobacco use during pregnancy, parental substance use in the past 6 months (i.e., tobacco, being drunk, and other drugs), psychopathological factors (i.e., internalizing, externalizing, total problem behaviors, DSM-5, and stress), and UPPS-P impulsive behavior scales were associated with higher odds of early-age tobacco use susceptibility.
Table 2 presents the multivariate results from separate nested hierarchical models that incrementally adjusted influencing factors of tobacco use susceptibility. In the baseline model that only included sociodemographic factors, females (vs. males, AOR [95% CI] = 0.69 [0.61–0.78]) had lower odds of reporting tobacco use susceptibility, while higher parental education (high school diploma or GED or bachelor’s degree or postgraduate vs. less than high school) was associated with increased risk of tobacco use susceptibility. In the fully adjusted model, females had lower odds of early-age tobacco use susceptibility (AOR [95% CI] = 0.80 [0.70–0.91]) than males. Positive parental monitoring (AOR [95% CI] = 0.76 [0.66–0.87]) and favorable school environment (AOR [95% CI] = 0.95 [0.93–0.98]) were associated with lower odds of early-age tobacco use susceptibility. In contrast, higher parental education (high school diploma or GED, AOR [95% CI] = 1.52 [1.02–2.28]; bachelor’s degree, AOR [95% CI] = 1.53 [1.03–2.28]; postgraduate degree, AOR [95% CI] = 1.54 [1.03–2.30] vs. less than high school), youth substance use behaviors (AOR [95% CI] = 2.24 [1.95–2.58]), internalizing factors (AOR [95% CI] = 1.03 [1.00–1.06]), UPPS-P impulsive behavior subscales (negative urgency (AOR [95% CI] = 1.1 [1.07e1.13]), lack of premeditation (AOR [95% CI] = 1.05 [1.02–1.08]), sensation seeking (AOR [95% CI] = 1.05 [1.02–1.08]), and positive urgency (AOR [95% CI] = 1.04 [1.01–1.06])) were associated with higher odds of early-age tobacco use susceptibility. The model c-statistic increased from 0.599 in the base sociodemographic model to 0.693 when youth substance and psychosocial factors were included, and further increased from 0.694 with the inclusion of parental substance use to 0.706 when social contextual factors were added.
Table 2.
Nested hierarchical modela of tobacco use susceptibility (n = 10,449)
| AOR and 95% CIa | Model I (sociodemographics) | Model II (model I + youth substance use & psychosocial) | Model III (model II + parental substance use) | Model IV (model III + social context) | Model V (model IV + neighborhood) |
|---|---|---|---|---|---|
|
| |||||
| Age, in months | 0.99 (0.99–1.00) | 0.99 (0.99–1.00) | 0.99 (0.98–1.00)* | 0.99 (0.98–1.00) | 0.99 (0.98–1.00) |
| Gender female (vs. male) | 0.69 (0.61–0.78)*** | 0.76 (0.67–0.87)*** | 0.75 (0.65–0.85)*** | 0.80 (0.70–0.91)** | 0.80 (0.70–0.91)*** |
| Race/ethnicity | |||||
| White | Reference | Reference | Reference | Reference | Reference |
| Black | 0.85 (0.69–1.04) | 0.96 (0.77–1.19) | 1.01 (0.80–1.27) | 0.98 (0.78–1.23) | 0.95 (0.76–1.20) |
| Hispanics | 0.94 (0.77–1.14) | 0.97 (0.79–1.18) | 0.97 (0.79–1.20) | 0.98 (0.79–1.21) | 0.97 (0.79–1.20) |
| Asians | 0.87 (0.58–1.30) | 0.98 (0.65–1.48) | 0.98 (0.63–1.52) | 0.93 (0.59–1.46) | 0.93 (0.59–1.47) |
| Othesrb | 1.09 (0.87–1.36) | 1.16 (0.93–1.45) | 1.18 (0.93–1.49) | 1.18 (0.93–1.49) | 1.17 (0.93–1.49) |
| Parental education level | |||||
| Less than high school | Reference | Reference | Reference | Reference | Reference |
| High school diploma or GED | 1.53 (1.08–2.16) | 1.41 (0.98–2.02) | 1.50 (1.01–2.24)* | 1.53 (1.02–2.29) | 1.52 (1.02–2.28)* |
| Some college or associate degree | 1.29 (0.93–1.78) | 1.23 (0.88–1.71) | 1.29 (0.89–1.89) | 1.30 (0.89–1.89) | 1.30 (0.89–1.89) |
| Bachelor’s degree | 1.53 (1.09–2.16)* | 1.43 (1.00–2.03)* | 1.51 (1.02–2.24)* | 1.54 (1.04–2.28)* | 1.53 (1.03–2.28)* |
| Postgraduate degree | 1.50 (1.06–2.13)* | 1.42 (0.99–2.03) | 1.51 (1.02–2.25)* | 1.55 (1.04–2.31)* | 1.54 (1.03–2.30)* |
| Family income | |||||
| <$25,000 | Reference | Reference | Reference | Reference | Reference |
| $25,000–$49,999 | 0.87 (0.69–1.11) | 0.81 (0.64–1.04) | 0.79 (0.61–1.03) | 0.80 (0.61–1.04) | 0.80 (0.62–1.05) |
| $50,000–$74,999 | 1.01 (0.79–1.29) | 0.98 (0.76–1.26) | 0.97 (0.74–1.27) | 0.98 (0.75–1.29) | 1.00 (0.77–1.32) |
| $75,000–$99,999 | 1.00 (0.77–1.29) | 0.98 (0.75–1.28) | 0.95 (0.71–1.26) | 0.96 (0.72–1.27) | 0.98 (0.74–1.31) |
| $100,000+ | 0.82 (0.64–1.05) | 0.78 (0.60–1.00) | 0.78 (0.60–1.03) | 0.80 (0.61–1.06) | 0.83 (0.63–1.09) |
| Don’t know or refuse – answer | 0.87 (0.66–1.14) | 0.84 (0.63–1.12) | 0.87 (0.64–1.19) | 0.88 (0.64–1.20) | 0.88 (0.64–1.21) |
| Family difficulty | 1.07 (0.89–1.29) | 1.00 (0.82–1.22) | 0.99 (0.81–1.23) | 0.98 (0.80–1.22) | 0.98 (0.79–1.21) |
| Premature | 1.03 (0.88–1.21) | 1.05 (0.89–1.24) | 1.03 (0.87–1.23) | 1.02 (0.85–1.21) | 1.01 (0.85–1.21) |
| Youth substance ever usec | 2.31 (2.03–2.64)*** | 2.3 (2.01–2.65)*** | 2.25 (1.95–2.58)*** | 2.24 (1.95–2.58)*** | |
| Psychopathology | |||||
| Internalizing factors | 1.04 (1.01–1.07)** | 1.04 (1.01–1.07)* | 1.03 (1.00–1.06)* | 1.03 (1.00–1.06)* | |
| Externalizing factors | 1.02 (0.99–1.04) | 1.01 (0.98–1.04) | 1.01 (0.98–1.04) | 1.01 (0.98–1.04) | |
| Total problems factors | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) | |
| DSM-5 | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) | 1.00 (0.98–1.01) | 1.00 (0.98–1.01) | |
| Stress | 0.99 (0.95–1.03) | 1.00 (0.95–1.04) | 1.00 (0.96–1.05) | 1.01 (0.96–1.05) | |
| UPPS-P_ Negative urgency | 1.10 (1.07–1.13)*** | 1.10 (1.07–1.13)*** | 1.10 (1.07–1.13)*** | 1.10 (1.07–1.13)*** | |
| UPPS-P_ Lack of premeditation | 1.07 (1.04–1.10)*** | 1.07 (1.04–1.10)*** | 1.05 (1.02–1.08)** | 1.05 (1.02–1.08)** | |
| UPPS-P_ Sensation seeking | 1.05 (1.03–1.08)*** | 1.04 (1.02–1.07)*** | 1.05 (1.02–1.08)*** | 1.05 (1.02–1.08)*** | |
| UPPS-P_ Positive urge | 1.04 (1.01–1.06)** | 1.04 (1.01–1.06)** | 1.03 (1.01–1.06)** | 1.04 (1.01–1.06)** | |
| UPPS-P_ Lack of perseverance | 1.06 (1.03–1.09)*** | 1.05 (1.02–1.08)** | 1.03 (0.99–1.06) | 1.03 (0.99–1.06) | |
| Tobacco use during the pregnancy | 1.04 (0.84–1.29) | 1.02 (0.82–1.27) | 1.02 (0.82–1.27) | ||
| Parental tobacco use | 1.05 (0.84–1.32) | 1.03 (0.82–1.29) | 1.03 (0.82–1.29) | ||
| Parental excessive alcohol use | 1.09 (0.93–1.26) | 1.09 (0.94–1.27) | 1.09 (0.94–1.27) | ||
| Parental other drug use | 1.21 (0.95–1.55) | 1.20 (0.94–1.53) | 1.19 (0.93–1.52) | ||
| Positive parenting—acceptance scale | 0.91 (0.73–1.13) | 0.91 (0.73–1.13) | |||
| Parental monitoring | 0.76 (0.67–0.88)*** | 0.76 (0.66–0.87)*** | |||
| School environment | 0.95 (0.93–0.98)*** | 0.95 (0.93–0.98)*** | |||
| Neighborhood perceptions | |||||
| Child | 1.01 (0.95–1.08) | ||||
| Parent | 0.95 (0.88–1.02) | ||||
| C-statistics (model goodness of fit) | 0.599 | 0.693 | 0.694 | 0.706 | 0.707 |
AOR = adjusted odds ratio; CI = confidence interval; GED = general educational development.
p < .05
p < .01
p < .001.
Separate survey logistic regression models were performed–incrementally adjust for predictors in the associations with the susceptibility to tobacco use. Individual subject’s inverse probability weighting score was included as weights.
The other category, based on the ABCD study definition, includes American Indian, Alaskan Native, Native Hawaiian, and other Pacific Islander as well as not otherwise listed (other) and multiracial individuals.
Included ever use of marijuana, alcohol, and other illicit drugs.
The results from nested hierarchical models stratified by males and females are presented in Tables 3 and 4. Internalizing factors were significantly associated with higher odds of tobacco use susceptibility among males (AOR [95% CI] = 1.04 [1.00–1.08]) but not among females. Parent’s perceived neighborhood safety was significantly associated with lower odds of tobacco use susceptibility among males (AOR [95% CI] = 0.89 [0.81–0.99]) but not among females, while a positive school environment was significantly associated with lower odds of tobacco use susceptibility among females (AOR [95% CI] = 0.92 [0.89–0.96]) but not among males. Other significant factors (i.e., youth substance use, AOR [95% CI] = 2.37 [1.98–2.85] for males and AOR [95% CI] = 2.11 [1.69–2.64] for females, UPPS-P impulsive behaviour subscales, AOR ranged from 1.04 to 1.09 for males and 1.05 to 1.11 for females, and parental monitoring, AOR [95% CI] = 0.78 [0.66–0.93] for males and AOR [95% CI] = 0.70 [0.55–0.88] for females) were similar between males and females. The model predictive ability was generally higher among females (c-statistic = 0.722 for the full model) than males (c-statistic = 0.697). The largest increase in model predictivity occurred when youth substance use and psychosocial factors were added into the hierarchical model for both males (c-statistic increased from 0.576 to 0.680) and females (c-statistic increased from 0.607 to 0.706).
Table 3.
Nested hierarchical modela of tobacco use susceptibility for males (n = 5,455)
| Male AOR and 95% CIa | Model I (sociodemographics) | Model II (model I + youth substance use & psychosocial) | Model III (model II + parental substance use) | Model IV (model III + social context) | Model V (model IV + neighborhood) |
|---|---|---|---|---|---|
|
| |||||
| Age, in months | 1.00 (0.99–1.01) | 0.99 (0.98–1.01) | 0.99 (0.98–1.00) | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) |
| Race/ethnicity | |||||
| White | Reference | Reference | Reference | Reference | Reference |
| Black | 0.85 (0.64–1.11) | 0.95 (0.71–1.26) | 1.00 (0.74–1.36) | 0.99 (0.73–1.33) | 0.94 (0.69–1.28) |
| Hispanics | 0.95 (0.74–1.22) | 0.95 (0.73–1.24) | 1.01 (0.77–1.33) | 1.02 (0.77–1.34) | 1.00 (0.76–1.31) |
| Asians | 0.67 (0.39–1.17) | 0.79 (0.46–1.38) | 0.80 (0.44–1.46) | 0.73 (0.39–1.36) | 0.74 (0.40–1.37) |
| Otherb | 1.03 (0.77–1.38) | 1.12 (0.84–1.50) | 1.09 (0.80–1.47) | 1.09 (0.81–1.48) | 1.09 (0.80–1.48) |
| Parental education level | |||||
| Less than high school | Reference | Reference | Reference | Reference | Reference |
| High school diploma or GED | 1.53 (0.95–2.48) | 1.39 (0.84–2.32) | 1.54 (0.86–2.73) | 1.57 (0.88–2.78) | 1.54 (0.87–2.74) |
| Some college or associate degree | 1.56 (0.99–2.47) | 1.52 (0.94–2.47) | 1.59 (0.92–2.76) | 1.61 (0.93–2.78) | 1.61 (0.93–2.77) |
| Bachelor’s degree | 1.71 (1.06–2.76)* | 1.6 (0.97–2.66) | 1.65 (0.93–2.92) | 1.69 (0.96–2.97) | 1.67 (0.95–2.93) |
| Postgraduate degree | 1.67 (1.03–2.71)* | 1.58 (0.95–2.63) | 1.62 (0.91–2.88) | 1.66 (0.94–2.94) | 1.64 (0.92–2.89) |
| Family income | |||||
| <$25,000 | Reference | Reference | Reference | Reference | Reference |
| $25,000–$49,999 | 0.89 (0.65–1.21) | 0.86 (0.62–1.18) | 0.84 (0.60–1.18) | 0.85 (0.60–1.20) | 0.86 (0.61–1.21) |
| $50,000–$74,999 | 0.93 (0.67–1.28) | 0.89 (0.64–1.24) | 0.85 (0.60–1.21) | 0.88 (0.62–1.25) | 0.90 (0.63–1.29) |
| $75,000–$99,999 | 1.12 (0.80–1.57) | 1.10 (0.78–1.56) | 1.05 (0.73–1.51) | 1.08 (0.75–1.57) | 1.13 (0.78–1.64) |
| $100,000+ | 0.84 (0.61–1.16) | 0.78 (0.56–1.09) | 0.78 (0.54–1.11) | 0.80 (0.56–1.15) | 0.83 (0.58–1.20) |
| Don’t know or refuse—answer | 0.77 (0.54–1.11) | 0.75 (0.51–1.11) | 0.81 (0.53–1.23) | 0.83 (0.54–1.27) | 0.83 (0.54–1.27) |
| Family difficulty | 1.11 (0.87–1.41) | 1.06 (0.82–1.36) | 0.98 (0.75–1.29) | 0.97 (0.74–1.27) | 0.96 (0.73–1.26) |
| Premature | 1.08 (0.88–1.32) | 1.08 (0.88–1.34) | 1.07 (0.85–1.33) | 1.05 (0.84–1.32) | 1.05 (0.83–1.31) |
| Youth substance ever usec | 2.39 (2.02–2.85)*** | 2.41 (2.01–2.89)*** | 2.36 (1.97–2.84)*** | 2.37 (1.98–2.85)*** | |
| Psychopathology | |||||
| Internalizing factors | 1.05 (1.01–1.08)* | 1.04 (1.01–1.08)* | 1.04 (1.00–1.08)* | 1.04 (1.00–1.08)* | |
| Externalizing factors | 1.02 (0.99–1.05) | 1.01 (0.98–1.05) | 1.01 (0.98–1.05) | 1.01 (0.98–1.05) | |
| Total problems factors | 0.99 (0.97–1.01) | 0.99 (0.97–1.01) | 0.99 (0.97–1.01) | 0.99 (0.97–1.01) | |
| DSM-5 | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) | 1.00 (0.98–1.01) | 1.00 (0.98–1.01) | |
| Stress | 0.99 (0.94–1.05) | 0.99 (0.93–1.06) | 1.00 (0.94–1.07) | 1.00 (0.94–1.07) | |
| UPPS-P_ Negative urgency | 1.09 (1.05–1.13)*** | 1.09 (1.05–1.13)*** | 1.09 (1.05–1.13)*** | 1.09 (1.05–1.13)*** | |
| UPPS-P_ Lack of premeditation | 1.07 (1.03–1.11)*** | 1.07 (1.03–1.12)*** | 1.05 (1.01–1.10)** | 1.05 (1.01–1.10)** | |
| UPPS-P_ Sensation seeking | 1.05 (1.02–1.09)*** | 1.05 (1.01–1.08)** | 1.05 (1.02–1.09)** | 1.05 (1.02–1.09)** | |
| UPPS-P_ Positive urge | 1.04 (1.01–1.07)* | 1.04 (1.01–1.07)* | 1.04 (1.00–1.07)* | 1.04 (1.00–1.07)* | |
| UPPS-P_ Lack of perseverance | 1.05 (1.01–1.09)* | 1.04 (1.00–1.08) | 1.02 (0.98–1.07) | 1.02 (0.98–1.07) | |
| Tobacco use during the pregnancy | 1.02 (0.76–1.36) | 1.00 (0.74–1.35) | 0.99 (0.74–1.33) | ||
| Parental tobacco use | 0.98 (0.73–1.32) | 0.97 (0.72–1.30) | 0.95 (0.71–1.29) | ||
| Parental excessive alcohol use | 1.20 (0.99–1.46) | 1.21 (0.99–1.46) | 1.21 (1.00–1.47) | ||
| Parental other drug use | 1.39 (1.00–1.93)* | 1.35 (0.98–1.88) | 1.35 (0.97–1.87) | ||
| Positive parenting-acceptance scale | 0.81 (0.61–1.07) | 0.8 (0.60–1.06) | |||
| Parental monitoring | 0.79 (0.67–0.94)** | 0.78 (0.66–0.93)** | |||
| School environment | 0.98 (0.95–1.01) | 0.98 (0.95–1.01) | |||
| Neighborhood perceptions | |||||
| Child | 1.05 (0.97–1.14) | ||||
| Parent | 0.89 (0.81–0.99)* | ||||
| C-statistics (model goodness of fit) | 0.576 | 0.68 | 0.685 | 0.695 | 0.697 |
AOR = adjusted odds ratio; CI = confidence interval; GED = general educational development.
p < .05
p < .01
p < .001.
Separate survey logistic regression models were performed—incrementally adjust for predictors in the associations with the susceptibility to tobacco use. Individual subject’s inverse probability weighting score was included as weights.
The other category, based on the ABCD Study definition, includes American Indian, Alaskan Native, Native Hawaiian, and other Pacific Islander as well as not otherwise listed (other) and multiracial individuals.
Included ever use of marijuana, alcohol, and other illicit drugs.
Table 4.
Nested hierarchical modela of tobacco use susceptibility for females (n = 4,994)
| Female AOR and 95% CIa | Model I (sociodemographics) | Model II (model I + youth substance use & psychosocial) | Model III (model II + parental substance use) | Model IV (model III + social context) | Model V (model IV + neighborhood) |
|---|---|---|---|---|---|
|
| |||||
| Age, in months | 0.99 (0.98–1.00) | 0.99 (0.98–1.00) | 0.99 (0.98–1.00) | 0.99 (0.98–1.01) | 0.99 (0.98–1.01) |
| Race/ethnicity | |||||
| White | Reference | Reference | Reference | Reference | Reference |
| Black | 0.85 (0.62–1.17) | 0.98 (0.70–1.36) | 1.04 (0.72–1.48) | 0.99 (0.69–1.41) | 0.97 (0.68–1.39) |
| Hispanics | 0.92 (0.68–1.27) | 1.02 (0.74–1.39) | 0.94 (0.67–1.32) | 0.97 (0.69–1.35) | 0.97 (0.70–1.36) |
| Asians | 1.22 (0.68–2.17) | 1.30 (0.69–2.42) | 1.25 (0.64–2.43) | 1.27 (0.65–2.48) | 1.29 (0.66–2.51) |
| Otherb | 1.18 (0.84–1.66) | 1.25 (0.88–1.77) | 1.33 (0.92–1.93) | 1.35 (0.93–1.95) | 1.34 (0.92–1.95) |
| Parental education level | |||||
| Less than high school | Reference | Reference | Reference | Reference | Reference |
| High school diploma or GED | 1.55 (0.93–2.58) | 1.44 (0.86–2.40) | 1.47 (0.83–2.6) | 1.47 (0.82–2.61) | 1.48 (0.83–2.64) |
| Some college or associate degree | 1.01 (0.63–1.63) | 0.94 (0.58–1.50) | 1.00 (0.59–1.71) | 0.98 (0.57–1.68) | 0.98 (0.57–1.68) |
| Bachelor’s degree | 1.38 (0.83–2.28) | 1.26 (0.76–2.08) | 1.39 (0.8–2.42) | 1.37 (0.78–2.42) | 1.39 (0.79–2.45) |
| Postgraduate degree | 1.37 (0.82–2.27) | 1.28 (0.77–2.13) | 1.45 (0.83–2.54) | 1.43 (0.81–2.54) | 1.46 (0.82–2.59) |
| Family income | |||||
| <$25,000 | Reference | Reference | Reference | Reference | Reference |
| $25,000–$49,999 | 0.84 (0.58–1.22) | 0.75 (0.52–1.10) | 0.72 (0.47–1.09) | 0.72 (0.47–1.09) | 0.73 (0.48–1.11) |
| $50,000–$74,999 | 1.12 (0.76–1.63) | 1.10 (0.74–1.62) | 1.14 (0.75–1.73) | 1.13 (0.74–1.73) | 1.14 (0.74–1.75) |
| $75,000–$99,999 | 0.81 (0.54–1.22) | 0.80 (0.52–1.23) | 0.79 (0.50–1.26) | 0.79 (0.5–1.26) | 0.80 (0.50–1.28) |
| $100,000+ | 0.79 (0.54–1.15) | 0.78 (0.53–1.15) | 0.79 (0.51–1.21) | 0.81 (0.52–1.25) | 0.82 (0.53–1.28) |
| Don’t know or refuse-answer | 0.98 (0.65–1.48) | 0.96 (0.62–1.47) | 0.93 (0.58–1.49) | 0.92 (0.57–1.48) | 0.92 (0.57–1.48) |
| Family difficulty | 1.04 (0.77–1.41) | 0.95 (0.69–1.31) | 1.01 (0.72–1.43) | 1.00 (0.70–1.42) | 1.00 (0.71–1.42) |
| Premature | 0.98 (0.76–1.25) | 1.01 (0.78–1.30) | 1.00 (0.76–1.31) | 0.98 (0.75–1.29) | 0.99 (0.75–1.30) |
| Youth substance ever usec | 2.24 (1.81–2.77)*** | 2.2 (1.77–2.74)*** | 2.13 (1.7–2.66)*** | 2.11 (1.69–2.64)*** | |
| Psychopathology | |||||
| Internalizing factors | 1.04 (0.99–1.09) | 1.03 (0.98–1.08) | 1.02 (0.97–1.07) | 1.02 (0.97–1.07) | |
| Externalizing factors | 1.01 (0.97–1.06) | 1.00 (0.96–1.05) | 1.00 (0.95–1.05) | 1.00 (0.95–1.05) | |
| Total problems factors | 1.00 (0.97–1.02) | 1.00 (0.97–1.03) | 1.00 (0.97–1.02) | 1.00 (0.97–1.03) | |
| DSM-5 | 0.99 (0.97–1.01) | 0.99 (0.97–1.01) | 1.00 (0.97–1.02) | 1.00 (0.97–1.02) | |
| Stress | 0.98 (0.91–1.05) | 0.99 (0.92–1.07) | 1.00 (0.93–1.08) | 1.00 (0.92–1.08) | |
| UPPS-P_ Negative urgency | 1.12 (1.08–1.17)*** | 1.12 (1.08–1.17)*** | 1.11 (1.06–1.16)*** | 1.11 (1.07–1.16)*** | |
| UPPS-P_ Lack of premeditation | 1.06 (1.02–1.11)** | 1.07 (1.02–1.13)** | 1.05 (1.00–1.10) | 1.05 (1.00–1.10) | |
| UPPS-P_ Sensation seeking | 1.05 (1.01–1.09)** | 1.04 (1.00–1.08)* | 1.05 (1.01–1.09)* | 1.05 (1.01–1.09)* | |
| UPPS-P_ Positive urge | 1.04 (1.00–1.08) | 1.03 (0.99–1.07) | 1.03 (0.99–1.07) | 1.03 (0.99–1.07) | |
| UPPS-P_ Lack of perseverance | 1.06 (1.02–1.11)** | 1.06 (1.01–1.11)* | 1.03 (0.98–1.08) | 1.03 (0.98–1.08) | |
| Tobacco use during the pregnancy | 1.02 (0.73–1.42) | 1.00 (0.72–1.39) | 1.01 (0.72–1.40) | ||
| Parental tobacco use | 1.15 (0.81–1.63) | 1.13 (0.79–1.60) | 1.12 (0.79–1.60) | ||
| Parental excessive alcohol use | 0.99 (0.78–1.26) | 1.00 (0.78–1.27) | 0.99 (0.78–1.27) | ||
| Parental other drug use | 1.01 (0.70–1.47) | 1.03 (0.71–1.51) | 1.04 (0.71–1.52) | ||
| Positive parenting-acceptance scale | 1.07 (0.76–1.51) | 1.08 (0.76–1.52) | |||
| Parental monitoring | 0.69 (0.55–0.87)** | 0.7 (0.55–0.88)** | |||
| School environment | 0.92 (0.89–0.96)*** | 0.92 (0.89–0.96)*** | |||
| Neighborhood perceptions | |||||
| Child | 0.96 (0.86–1.06) | ||||
| Parent | 1.01 (0.90–1.13) | ||||
| C-statistics (model goodness of fit) | 0.607 | 0.706 | 0.706 | 0.721 | 0.722 |
AOR = adjusted odds ratio; CI = confidence interval; GED = general educational development.
p < .05
p < .01
p < .001.
Separate survey logistic regression models were performed—incrementally adjusted for predictors in the associations with the susceptibility to tobacco use. Individual subject’s inverse probability weighting score was included as weights.
The other category, based on the ABCD Study definition, includes American Indian, Alaskan Native, Native Hawaiian, and other Pacific Islander as well as not otherwise listed (other) and multiracial individuals.
Included ever use of marijuana, alcohol, and other illicit drugs.
Discussion
Early adolescence is a critical developmental stage for preventing tobacco initiation. Since susceptibility to tobacco use can predict the risk of smoking initiation as many as 3–4 years before the first try [7,12,13], an understanding of the risk and protective factors associated with early-age tobacco susceptibility is needed to develop tobacco control policies and effective prevention strategies to curb youth tobacco use. This study analyzed a large number of 9–10-year-old children in a national sample from the ABCD study to report that nearly one-sixth of tobacco naïve youths were susceptible to tobacco use. From the social cognitive perspective [19], child behaviors are developed under a social context with a dynamic and reciprocal interaction of the individual factors, family structure, parental function, and environmental influences. Our study documented differential functionality in early-age tobacco use susceptibility across these domains by leveraging the hierarchical models.
After incrementally adjusting for a variety of covariates, being male, other substance use, and psychosocial factors (e.g., internalizing problems and UPPS-P negative urgency, lack of premeditation, sensation seeking, and positive urgency) were associated with increased early-age susceptibility to tobacco use. Children with poor psychosocial issues may seek tobacco use as a coping strategy. Under intensive tobacco marketing, vulnerable adolescents might consider tobacco use to produce positive effects, reduce stress, and gain social popularity, which could promote early-age tobacco initiation. Consistent with previous literature, [3,27] our study also found that 9–10-year-old children of college graduates were more likely to be susceptible to any tobacco use than those with less than a high school education, which is contradictory to the established education association with smoking behavior [47]. It is possible that children from higher educated families are more likely to be susceptible but less likely to progress from experimentation to dependent use. But another possibility is that these data reflect the changing tobacco use landscape with children from higher educated families being more willing to try less harmful e-cigarettes [3].
This study adds to the growing body of literature by indicating such influencing factors might be rooted in the very early life stage with profound impacts on brain and behavioral development. Parental monitoring and a positive school environment were strong protective factors that are associated with reduced susceptibility to tobacco use. The parental influences on tobacco use are likely to decrease as youth move into the 12–14-year-old category, where peer influences become more important than parental influences [48,49]. Therefore, it is essential to educate parents about the harm of tobacco use on child’s development, nicotine addiction, and long-term adverse effects on tobacco-related morbidity and mortality. Parental monitoring will help reduce youth experimenting risky behaviors due to sensation seeking and negative urgency.
Environmental factors influence youth health behaviors [50,51] and are associated with neurocognitive performances [52]. We further extended previous literature by examining whether perceived neighborhood safety is another protective factor for youth early-age susceptibility to tobacco use. Child’s perceptions of a safe neighborhood were associated with lower odds of youth susceptibility to tobacco use in the univariate analysis, but the association became insignificant after adjusting for other covariates and had a small incremental contribution to the model predictivity, suggesting that other factors, such as parental monitoring and positive school environment, may mediate the influences from poor neighborhood safety perception to tobacco susceptibility.
We further conducted stratified analyses to examine gender-difference in susceptibility to tobacco use. Youth substance use, psychosocial factors, and parental monitoring influence both males and females in a similar manner. However, several factors had heterogeneous effects between males and females. For instance, internalizing factors were significantly associated with higher odds of tobacco use susceptibility for males but not for females. A positive school environment was associated with lower odds of tobacco use susceptibility for females but not for males. Further studies are needed to test interventions that moderate these factors to assess those that are associated with lowering youth tobacco use.
One of the strength of this study is the inclusion of a robust coverage of 27 different influencing factors in the hierarchical models to incrementally account for sociodemographics, psychosocial factors, substance use behaviors, proximal and familiar contacts, and perceived neighborhood safety. This study has limitations. First, this study analyzed the cross-sectional ABCD study data at baseline; thus, the causal inference cannot be established. Second, tobacco use behaviors were self-reported, subject to social desirability biases, especially for younger respondents [53]. However, the test and retest reliability of self-reported behaviors related to tobacco use among adolescents is high [53]. Third, susceptibility to tobacco use was assessed by any tobacco product without differentiating the type of tobacco products. Future studies should assess the relationship between early-age susceptibility and initiation of each type of tobacco product, especially for e-cigarettes and other emerging tobacco products. Finally, our study focused on the early-age youth, while tobacco use susceptibility might change over time. Further research on whether and to what extent these influencing factors on tobacco use susceptibility might change from childhood to late adolescents is warranted.
Despite these limitations, this study is the first to examine a set of risk and protective factors for tobacco use susceptibility among children aged 9 and 10 years. Gender, youth substance use, psychosocial factors, parental monitoring, positive school environment, and perception of neighborhood safety are robustly associated with youth susceptibility to tobacco use with some gender differences. Integration of these influencing factors in family and school education efforts may increase the efficacy of youth tobacco prevention and improve long-term health outcome.
Supplementary Material
IMPLICATIONS AND CONTRIBUTION.
Using a large-scale national sample, this study found that 16.7% of 9–10-year-old children were susceptible to tobacco use. Parental monitoring, environmental and psychosocial factors play pivotal roles in early childhood tobacco susceptibility. Integration of these factors in family and school education efforts may increase the efficacy of youth tobacco prevention.
Funding sources
Research reported in this publication was supported by NIDA and FDA Center for Tobacco Products (CTP) under Award Number R21DA054818 (PI: Dai) and by the Tobacco Related Disease Research Program (TRDRP) grant under Award Number T31IR1584 (PI: Pierce). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the Food and Drug Administration. The Adolescent Brain Cognitive Development (ABCD) Study was supported by the NIH and other federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. The ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this manuscript. The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Conflicts of interest: The authors have no conflicts of interest to disclose.
Disclaimer: The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Clinical Trial Registration
This is not a clinical trial.
Supplementary Data
Supplementary data related to this article can be found at 10.1016/j.jadohealth.2022.09.021.
Access to Data and Data Analysis
Dai had full access to all the data in the study and takes responsibility for the integrity of the data.
References
- [1].U.S. Department of Health and Human Services. Preventing tobacco use among youth and young adults: A report of the surgeon general. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2012. [Google Scholar]
- [2].Pierce JP, Chen R, Leas EC, et al. Use of E-cigarettes and other tobacco products and progression to daily cigarette smoking. Pediatrics 2021;147. e2020025122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Trinidad DR, Pierce JP, Sargent JD, et al. Susceptibility to tobacco product use among youth in wave 1 of the population assessment of tobacco and health (PATH) study. Prev Med 2017;101:8–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Johnston LD, Miech RA, O’Malley PM, et al. Monitoring the future national survey results on drug use, 1975–2020: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, University of Michigan; 2021. [Google Scholar]
- [5].The Centers for Disease Control and Prevention. National youth tobacco survey (NYTS). Available at: https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/index.htm. Accessed April 26, 2022.
- [6].Volkow ND, Koob GF, Croyle RT, et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev Cogn Neurosci 2018;32:4–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Pierce JP, Choi WS, Gilpin EA, et al. Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol 1996;15:355–61. [DOI] [PubMed] [Google Scholar]
- [8].Pierce JP, Farkas A, Evans N, et al. California Tobacco Survey 1992: A focus on preventing uptake in adolescents. In: . Sacramento: California Department of Health Services; 1993. [Google Scholar]
- [9].Smith RE, Swinyard WR. Cognitive response to advertising and trial: Belief strength, belief confidence and product curiosity. J Advertising 1988;17:3–14. [Google Scholar]
- [10].Pierce JP, Choi WS, Gilpin EA, et al. Tobacco industry promotion of cigarettes and adolescent smoking. JAMA 1998;279:511–5. [DOI] [PubMed] [Google Scholar]
- [11].Pierce JP, Distefan JM, Kaplan RM, et al. The role of curiosity in smoking initiation. Addict Behav 2005;30:685–96. [DOI] [PubMed] [Google Scholar]
- [12].Strong DR, Hartman SJ, Nodora J, et al. Predictive validity of the expanded susceptibility to smoke index. Nicotine Tob Res 2015;17:862–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Nodora J, Hartman SJ, Strong DR, et al. Curiosity predicts smoking experimentation independent of susceptibility in a US national sample. Addict Behav 2014;39:1695–700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Pierce JP, Sargent JD, Portnoy DB, et al. Association between receptivity to tobacco advertising and progression to tobacco use in youth and young adults in the PATH study. JAMA Pediatr 2018;172:444–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Gentzke AS, Creamer M, Cullen KA, et al. Vital signs: Tobacco product use among middle and high school students - United States, 2011–2018. MMWR Morb Mortal Wkly Rep 2019;68:157–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Gentzke AS, Wang TW, Jamal A, et al. Tobacco product use among middle and high school students - United States, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1881–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Truth Initiative. E-cigarettes: Facts, stats and regulations. Available at: https://truthinitiative.org/research-resources/emerging-tobacco-products/e-cigarettes-facts-stats-and-regulations. Accessed January 31, 2022. [Google Scholar]
- [18].Bold KW, Kong G, Cavallo DA, et al. E-cigarette susceptibility as a predictor of youth initiation of E-cigarettes. Nicotine Tob Res 2017;20:140–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Bandura A Social foundations of thought and action: A social cognitive theory. NJ: Prentice-hall Englewood Cliffs; 1986. [Google Scholar]
- [20].Richardson JL, Radziszewska B, Dent CW, et al. Relationship between afterschool care of adolescents and substance use, risk taking, depressed mood, and academic achievement. Pediatrics 1993;92:32–8. [PubMed] [Google Scholar]
- [21].Crone MR, Reijneveld SA. The association of behavioural and emotional problems with tobacco use in adolescence. Addict Behav 2007;32: 1692–8. [DOI] [PubMed] [Google Scholar]
- [22].Stanton CA, Highland KB, Tercyak KP, et al. Authoritative parenting and cigarette smoking among multiethnic preadolescents: The mediating role of anti-tobacco parenting strategies. J Pediatr Psychol 2014;39:109–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Ortiz C, Lopez-Cuadrado T, Rodriguez-Blazquez C, et al. Physical and social environmental factors related to co-occurrence of unhealthy lifestyle behaviors. Health Place 2022;75:102804. [DOI] [PubMed] [Google Scholar]
- [24].Meier KS. Tobacco truths: The impact of role models on children’s attitudes toward smoking. Health Educ Q 1991;18:173–82. [DOI] [PubMed] [Google Scholar]
- [25].Cohen EL, Shumate MD, Gold A. Original: Anti-smoking media campaign messages: Theory and practice. Health Commun 2007;22:91–102. [DOI] [PubMed] [Google Scholar]
- [26].Svensson R Gender differences in adolescent drug use: The impact of parental monitoring and peer deviance. Youth Soc 2003;34:300–29. [Google Scholar]
- [27].Pierce JP, Sargent JD, White MM, et al. Receptivity to tobacco advertising and susceptibility to tobacco products. Pediatrics 2017;139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Garavan H, Bartsch H, Conway K, et al. Recruiting the ABCD sample: Design considerations and procedures. Dev Cogn Neurosci 2018;32: 16–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Barch DM, Albaugh MD, Avenevoli S, et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev Cogn Neurosci 2018;32: 55–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Feldstein Ewing SW, Chang L, Cottler LB, et al. Approaching retention within the ABCD study. Dev Cogn Neurosci 2018;32:130–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Lisdahl KM, Sher KJ, Conway KP, et al. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods. Dev Cogn Neurosci 2018;32:80–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Achenbach TM. The Achenbach system of empirically based assessment (ASEBA): Development, findings, theory, and applications. Burlington: University of Vermont, Research Center for Children, Youth, & Families; 2009. [Google Scholar]
- [33].Association AP. DSM 5 diagnostic and statistical manual of mental disorders 2013;947:947. [Google Scholar]
- [34].Aseba.org. School-Age (CBCL, TRF, YSR, BPM/6–18). 2021. Available at: https://aseba.org/school-age/. Accessed September 8, 2022. [Google Scholar]
- [35].Whiteside SP, Lynam DR. The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Pers Individ Dif 2001;30:669–89. [Google Scholar]
- [36].Schaefer ES. Children’s reports of parental behavior: An inventory. Child Dev 1965;36:413–24. [PubMed] [Google Scholar]
- [37].Karoly HC, Callahan T, Schmiege SJ, et al. Evaluating the Hispanic paradox in the context of adolescent risky sexual behavior: The role of parent monitoring. J Pediatr Psychol 2016;41:429–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Arthur MW, Briney JS, Hawkins JD, et al. Measuring risk and protection in communities using the communities that care youth survey. Eval Program Plann 2007;30:197–211. [DOI] [PubMed] [Google Scholar]
- [39].Echeverria SE, Diez-Roux AV, Link BG. Reliability of self-reported neighborhood characteristics. J Urban Health 2004;81:682–701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [40].Zucker RA, Gonzalez R, Feldstein Ewing SW, et al. Assessment of culture and environment in the adolescent brain and cognitive development study: Rationale, description of measures, and early data. Dev Cogn Neurosci 2018;32:107–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [41].McHugh ML. The chi-square test of independence. Biochem Med (Zagreb) 2013;23:143–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [42].Heeringa SG, Berglund PA. A guide for population-based analysis of the adolescent brain cognitive development (ABCD) Study baseline data. Bio-Rxiv 2020. 10.1101/2020.02.10.942011. [DOI] [Google Scholar]
- [43].Dai H, Ingram DG, Taylor JB. Hierarchical and mediation analysis of disparities in very short sleep among sexual minority youth in the US, 2015. Behav Sleep Med 2019;18:1–14. [DOI] [PubMed] [Google Scholar]
- [44].Scoggins D, Khan AS, Dai H. Hierarchical analysis of disparities in suicidal outcomes with intersection of sexual minority and gender among U.S. Youth, 2017. Health Educ Behav 2021;49:569–83. [DOI] [PubMed] [Google Scholar]
- [45].Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press; 2006. [Google Scholar]
- [46].du Prel JB, Hommel G, Rohrig B, et al. Confidence interval or p-value?: Part 4 of a series on evaluation of scientific publications. Dtsch Arztebl Int 2009; 106:335–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47].Pierce JP, Fiore MC, Novotny TE, et al. Trends in cigarette smoking in the United States. Educational differences are increasing. JAMA 1989;261:56–60. [PubMed] [Google Scholar]
- [48].Biddle BJ, Bank BJ, Marlin MM. Parental and peer influence on adolescents. Soc Forces 1980;58:1057–79. [Google Scholar]
- [49].Webster RA, Hunter M, Keats JA. Peer and parental influences on adolescents’ substance use: A path analysis. Int J Addict 1994;29:647–57. [DOI] [PubMed] [Google Scholar]
- [50].Ding D, Sallis JF, Kerr J, et al. Neighborhood environment and physical activity among youth a review. Am J Prev Med 2011;41:442–55. [DOI] [PubMed] [Google Scholar]
- [51].National Research Council. The science of adolescent risk-taking: Workshop report. Washington (DC): National Academies Press; 2011. [PubMed] [Google Scholar]
- [52].Hackman DA, Cserbik D, Chen JC, et al. Association of local variation in neighborhood disadvantage in metropolitan areas with youth neurocognition and brain structure. JAMA Pediatr 2021;175:e21 0426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [53].Brener ND, Billy JO, Grady WR. Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: Evidence from the scientific literature. J Adolesc Health 2003;33:436–57. [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
Dai had full access to all the data in the study and takes responsibility for the integrity of the data.